DeepSeek-OCR: Contexts Optical Compression (2025 · 54 citations)
Because attention cost grows quadratically with sequence length, long contexts are expensive for LLMs. The authors ask whether rendering text as an image could carry the same content in far fewer tokens. OCR serves as a measurable test: the model must recover the original text from a small set of vision tokens.…
DeepSeek-OCR 2: Visual Causal Flow (2026 · 8 citations)
Most vision-language models pass image patches to the LLM in a fixed top-left-to-bottom-right order. This ignores the logical structure of complex pages, such as multi-column layouts, tables and formulas. DeepSeek-OCR 2 addresses this by redesigning the encoder. Its DeepEncoder V2 keeps the compact SAM-based…
Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models (2026 · 3 citations)
Sparse autoencoders (SAEs) break LLM hidden states into a small number of interpretable directions. In practice, though, they have mostly been used to inspect and label features after the fact, not to build or fix models. The Qwen Team addresses this with Qwen-Scope, an openly released collection of SAEs trained on…

Co-authors here

9 co-authors with a page, the most papers together first.

Cited by

Papers of our library cite their works 178 times (of the 149,439 papers whose bibliographies we read); 22 citing papers with a page here →.

Updated 31 October 2026